title: Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks.
abstract: BACKGROUND
  Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard.
  
  
  METHODS
  In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively.
  
  
  RESULTS
  In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions.
  
  
  CONCLUSIONS
  The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer.
authors.affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: Md Fahmid
authors.initials: MF
authors.lastname: Islam
authors.affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: Md Moinul
authors.initials: MM
authors.lastname: Hoque
authors.affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: Rajat Suvra
authors.initials: RS
authors.lastname: Banik
authors.affiliation: Forestry and Wood Technology Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: Sanjoy
authors.initials: S
authors.lastname: Roy
authors.affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: Sharmin Sultana
authors.initials: SS
authors.lastname: Sumi
authors.affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: F M Nazmul
authors.initials: FMN
authors.lastname: Hassan
authors.affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: Md Tauhid Siddiki
authors.initials: MTS
authors.lastname: Tomal
authors.affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Ullah
authors.affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
authors.forename: K M Taufiqur
authors.initials: KMT
authors.lastname: Rahman